identity, permissions, and audit trails for your AI systems — powered by gatewaystack.
your AI tools work. but they run on shared API keys, there's no per-user identity, and you can't answer basic questions like "who accessed what data through the AI?" or "what did the model do on behalf of this user?"
this is the governance gap. every AI system hits it eventually — usually right when compliance asks, or right when something goes wrong.
a working governance layer integrated into your AI stack:
built on gatewaystack — the identity and policy layer i built to solve the three-party problem in AI systems: user ↔ model ↔ backend. the same system i run in my own production products. not a prototype — already deployed and serving real users.
architecture documentation — how the governance layer works, how to extend it, how to add new tools and scopes. written for your team.
session 1 (4 hours): we map your current AI stack, identify the governance gaps, and design the identity and permissions model together. i'll start building the layer live during the session.
async build (3-5 days): i implement the full governance layer — identity binding, permission enforcement, audit logging, rate limiting. daily updates, you can steer throughout.
session 2 (2 hours): we deploy together, run through the audit trail, test the permissions model, and walk your team through maintenance. everything pushed to your repo.
$10,000 — includes both sessions, async build, deployment, and documentation.
typical timeline: 1-2 weeks from kickoff to deployed. complexity depends on your existing auth setup and number of AI tools to govern.
assess: ai architecture audit · genai initiative prioritization · build vs buy analysis
build: idea-to-pilot sprint · mcp server build sprint · pilot-to-production sprint
hardening inner for production: identity, scopes, and audit trails →
book an intro call — 45 minutes, no cost. we'll map your current AI stack and i'll show you where the governance gaps are.